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Efficient solar power generation forecasting for greenhouses: A hybrid deep learning approach.
- Source :
- Alexandria Engineering Journal; Mar2024, Vol. 91, p222-236, 15p
- Publication Year :
- 2024
-
Abstract
- In this research paper, we propose a novel hybrid deep learning approach, SSA-CNN-LSTM, for forecasting solar power generation. The approach combines Singular Spectrum Analysis (SSA), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to leverage temporal and spatial dependencies in real-time greenhouse solar power generation data. Through a comprehensive comparative analysis, SSA-CNN-LSTM is compared against three established models, CNN-LSTM, SSA-CNN, and SSA-LSTM, employing real solar power generation data over a two-year period. The findings prominently demonstrate SSA-CNN-LSTM's exceptional performance, particularly in the 1-hour ahead prediction horizon. With an hour-ahead Mean Absolute Error (MAE) of 0.1202, SSA-CNN-LSTM surpasses the forecast precision of CNN-LSTM (0.6269), SSA-CNN (0.2354), and SSA-LSTM (0.2049). This excellence extends to the 2-hour-ahead forecast, where SSA-CNN-LSTM maintains its superiority with an MAE of 0.1400. In the day-ahead forecast, SSA-CNN-LSTM upholds its competitiveness, demonstrating an MAE of 0.1774. These outcomes underscore the immense potential of SSA-CNN-LSTM as a formidable tool for precise solar power forecasting. The model's effectiveness empowers greenhouse operators and energy management systems to optimize resource allocation, ultimately fostering elevated energy efficiency and overall greenhouse productivity. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 11100168
- Volume :
- 91
- Database :
- Supplemental Index
- Journal :
- Alexandria Engineering Journal
- Publication Type :
- Academic Journal
- Accession number :
- 176009070
- Full Text :
- https://doi.org/10.1016/j.aej.2024.02.004